Optimization of energy usage for fleet‐wide power generating system under carbon mitigation options
Bibliographic record
Abstract
Abstract This article presents a fleet‐wide model for energy planning that can be used to determine the optimal structure necessary to meet a given CO 2 reduction target while maintaining or enhancing power to the grid. The model incorporates power generation as well as CO 2 emissions from a fleet of generating stations (hydroelectric, fossil fuel, nuclear, and wind). The model is formulated as a mixed integer program and is used to optimize an existing fleet as well as recommend new additional generating stations, carbon capture and storage, and retrofit actions to meet a CO 2 reduction target and electricity demand at a minimum overall cost. The model was applied to the energy supply system operated by Ontario power generation (OPG) for the province of Ontario, Canada. In 2002, OPG operated 79 electricity generating stations; 5 are fueled with coal (with a total of 23 boilers), 1 by natural gas (4 boilers), 3 nuclear, 69 hydroelectric and 1 wind turbine generating a total of 115.8 TWh. No CO 2 capture process existed at any OPG power plant; about 36.7 million tonnes of CO 2 was emitted in 2002, mainly from fossil fuel power plants. Four electricity demand scenarios were considered over a span of 10 years and for each case the size of new power generation capacity with and without capture was obtained. Six supplemental electricity generating technologies have been allowed for: subcritical pulverized coal‐fired (PC), PC with carbon capture (PC+CCS), integrated gasification combined cycle (IGCC), IGCC with carbon capture (IGCC+CCS), natural gas combined cycle (NGCC), and NGCC with carbon capture (NGCC+CCS). The optimization results showed that fuel balancing alone can contribute to the reduction of CO 2 emissions by only 3% and a slight, 1.6%, reduction in the cost of electricity compared to a calculated base case. It was found that a 20% CO 2 reduction at current electricity demand could be achieved by implementing fuel balancing and switching 8 out of 23 coal‐fired boilers to natural gas. However, as demand increases, more coal‐fired boilers needed to be switched to natural gas as well as the building of new NGCC and NGCC+CCS for replacing the aging coal‐fired power plants. To achieve a 40% CO 2 reduction at 1.0% demand growth rate, four new plants (2 NGCC, 2 NGCC+CCS) as well as carbon capture processes needed to be built. If greater than 60% CO 2 reductions are required, NGCC, NGCC+CCS, and IGCC+CCS power plants needed to be put online in addition to carbon capture processes on coal‐fired power plants. The volatility of natural gas prices was found to have a significant impact on the optimal CO 2 mitigation strategy and on the cost of electricity generation. Increasing the natural gas prices resulted in early aggressive CO 2 mitigation strategies especially at higher growth rate demands. © 2009 American Institute of Chemical Engineers AIChE J, 2009
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".